Camera Calibration using a Collimator System

Abstract

Camera calibration is a crucial step in photogrammetry and 3D vision applications. In practical scenarios with a long working distance to cover a wide area, target-based calibration methods become complicated and inflexible due to site limitations. This paper introduces a novel camera calibration method using a collimator system, which can provide a reliable and controllable calibration environment for cameras with varying working distances. Based on the optical geometry of the collimator system, we prove that the relative motion between the target and camera conforms to the spherical motion model, reducing the original 6DOF relative motion to 3DOF pure rotation motion. Furthermore, a closed-form solver for multiple views and a minimal solver for two views are proposed for camera calibration. The performance of our method is evaluated in both synthetic and real-world experiments, which verify the feasibility of calibration using the collimator system and demonstrate that our method is superior to the state-of-the-art methods. Demo code is available at https://github.com/LiangSK98/CollimatorCalibration.

Figures (11)

• Figure 1: Diagram of the collimator-based calibration method. From the inherent optical geometric properties of the collimator, the relative motion between the target and camera can be proved to conform to the spherical motion model.
• Figure 1: Robustness comparison of initial value estimation with increasing noise level. The relative focal length error (Left) and absolute principal point error (Right) increase linearly with the increase in noise. The proposed algorithm demonstrates greater robustness than other algorithms.
• Figure 2: Diagram of collimator system. The geometric property of the collimator system leads to the angle invariance of any point pair on the reticle.
• Figure 2: Accuracy comparisons of initial value estimation with the number of images increases. The statistical results of relative focal length error (Left) and absolute principal point error (Right) indicate that the calibration results become more stable with increased images. Our algorithm demonstrates superior accuracy compared to others.
• Figure 3: Comparison of calibration errors with increasing noise level.